The promising learning performance in FL comes along with several challenges which are roughly divided into the three categories of privacy, statistical properties of local datasets and communication-efficiency. With respect to privacy, FL has been shown to be vulnerable against model inversion attack [1]}, [2]}. This follows from the fact that edge devices in FL share their local models that are often strongly correlated with their local data [3]}, [4]}. Statistical properties of local datasets further refers to the sensitivity of FL against data distribution. Initial studies on FL have shown promising performance and fast convergence of this framework for homogeneous local datasets. Nevertheless, obtaining these favorable behaviors have been shown to face some analytical challenges when we deviate from simple statistical models for local datasets [5]}. This is in particular crucial, as the exact statistical model of data is rather unknown in practice.
